SoroushVahidi/scidocs huggingface dataset:
Which Hotel attributes Matter ? A review of previous and a framework for future research
(2017)

A lot of effort has been made in the last decades to reveal, which hotel attributes guest care about. Due to the high costs that are typically involved with investments in the hotel industry, it makes a lot of sense to study, which product components the travellers appreciate. This study reveals that hotel attribute research turns out to be a wide and extremely heterogeneous field of research. The authors review empirical studies investigating the importance of hotel attributes, provide attribute rankings and suggest a framework for past and future research projects in the field, based on the dimensions “focus of research”, ”risk versus utility” and “trade-off versus no trade-off questioning situation”.
On Power Quality of Variable-Speed Constant-Frequency Aircraft Electric Power Systems
(2010)

In this paper, a comprehensive model of the variable-speed constant-frequency aircraft electric power system is developed to study the performance characteristics of the system and, in particular, the system power quality over a frequency range of operation of 400 Hz to 800 Hz. A fully controlled active power filter is designed to regulate the load terminal voltage, eliminate harmonics, correct supply power factor, and minimize the effect of unbalanced loads. The control algorithm for the active power filter (APF) is based on the perfect harmonic cancellation method which provides a three-phase reference supply current in phase with its positive-sequence fundamental voltage. The proposed APF is integrated into the model of a 90-kVA advanced aircraft electric power system under VSCF operation. The performance characteristics of the system are studied with the frequency of the generator's output voltage varied from 400 Hz to 800 Hz under different loading conditions. Several case studies are presented including dc loads as well as passive and dynamic ac loads. The power quality characteristics of the studied aircraft electric power system with the proposed active filter are shown to be in compliance with the most recent military aircraft electrical standards MIL-STD-704F as well as with the IEEE Std. 519.
Shape-aware Instance Segmentation
(2016)

We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation process, such as too small or shifted boxes. In this paper, we introduce a novel object segment representation based on the distance transform of the object masks. We then design an object mask network (OMN) with a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. This allows us to predict masks that go beyond the scope of the bounding boxes and are thus robust to inaccurate object candidates. We integrate our OMN into a Multitask Network Cascade framework, and learn the resulting shape-aware instance segmentation (SAIS) network in an end-to-end manner. Our experiments on the PASCAL VOC 2012 and the CityScapes datasets demonstrate the benefits of our approach, which outperforms the state-of-the-art in both object proposal generation and instance segmentation.
RT-Mover: a rough terrain mobile robot with a simple leg-wheel hybrid mechanism
(2011)

There is a strong demand in many fields for practical robots, such as a porter robot and a personal mobility robot, that can move over rough terrain while carrying a load horizontally. We have developed a robot, called RT-Mover, which shows adequate mobility performance on targeted types of rough terrain. It has four drivable wheels and two leg-like axles but only five active shafts. A strength of this robot is that it realizes both a leg mode and a wheel mode in a simple mechanism. In this paper, the mechanical design concept is discussed. With an emphasis on minimizing the number of drive shafts, a mechanism is designed for a four-wheeled mobile body that is widely used in practical locomotive machinery. Also, strategies for moving on rough terrain are proposed. The kinematics, stability, and control of RT-Mover are also described in detail. Some typical cases of rough terrain for wheel mode and leg mode are selected, and the robot’s ability of locomotion is assessed through simulations and experiments. In each case, the robot is able to move over rough terrain while maintaining the horizontal orientation of its platform.
Privacy attacks in social media using photo tagging networks: a case study with Facebook
(2012)

Social-networking users unknowingly reveal certain kinds of personal information that malicious attackers could profit from to perpetrate significant privacy breaches. This paper quantitatively demonstrates how the simple act of tagging pictures on the social-networking site of Facebook could reveal private user attributes that are extremely sensitive. Our results suggest that photo tags can be used to help predicting some, but not all, of the analyzed attributes. We believe our analysis make users aware of significant breaches of their privacy and could inform the design of new privacy-preserving ways of tagging pictures on social-networking sites.
A framework based on RSA and AES encryption algorithms for cloud computing services
(2014)

Cloud computing is an emerging computing model in which resources of the computing communications are provided as services over the Internet. Privacy and security of cloud storage services are very important and become a challenge in cloud computing due to loss of control over data and its dependence on the cloud computing provider. While there is a huge amount of transferring data in cloud system, the risk of accessing data by attackers raises. Considering the problem of building a secure cloud storage service, current scheme is proposed which is based on combination of RSA and AES encryption methods to share the data among users in a secure cloud system. The proposed method allows providing difficulty for attackers as well as reducing the time of information transmission between user and cloud data storage.
BCSAT : A Benchmark Corpus for Sentiment Analysis in Telugu Using Word-level Annotations
(2018)

The presented work aims at generating a systematically annotated corpus that can support the enhancement of sentiment analysis tasks in Telugu using wordlevel sentiment annotations. From OntoSenseNet, we extracted 11,000 adjectives, 253 adverbs, 8483 verbs and sentiment annotation is being done by language experts. We discuss the methodology followed for the polarity annotations and validate the developed resource. This work aims at developing a benchmark corpus, as an extension to SentiWordNet, and baseline accuracy for a model where lexeme annotations are applied for sentiment predictions. The fundamental aim of this paper is to validate and study the possibility of utilizing machine learning algorithms, word-level sentiment annotations in the task of automated sentiment identification. Furthermore, accuracy is improved by annotating the bi-grams extracted from the target corpus.
Effects of quiz-style information presentation on user understanding
(2007)

This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keep their attention on receiving information, it is expected that information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. This indicates that quiz-style information presentation promotes engagement in the interaction with the system, leading to the improved user understanding.
Research on continuous auditing: A bibliometric analysis
(2017)

This paper presents the results of a bibliometric study about the evolution of research on Continuous Auditing. This study has as main motivation to find reasons for the very slow evolvement of research on this topic. In addition, Continuous Auditing is one of the features of the emerging concept of Continuous Assurance. Thus, considering that Continuous Assurance represents numerous advantages for the organizational performance, this study also intends to understand if there is a relation between the evolution of research on Continuous Auditing and the still very low maturity levels of continuous assurance solutions. This study shows that the number of publications is considerably reduced and that the slow growth of research on Continuous Auditing may be contributing to the lack of maturity of Continuous Assurance.
Dispositional Factors in Internet Use: Personality Versus Cognitive Style
(2007)

This study directly tests the effect of personality and cognitive style on three measures of Internet use. The results support the use of personality—but not cognitive style—as an antecedent variable. After controlling for computer anxiety, selfefficacy, and gender, including the “Big Five” personality factors in the analysis significantly adds to the predictive capabilities of the dependent variables. Including cognitive style does not. The results are discussed in terms of the role of personality and cognitive style in models of technology adoption and use.
Voice quality evaluation of recent open source codecs
(2010)

• Averaged frequency responses at different 16, and 24 kHz. External sampling rate does not tell the internal sampling rate. • Supported signal bandwidth depends on bitrate, but no documentation exists bandwidths were found out expirementally • We tested 32kHz sampling with 16ms frame length. There is also 8 ms lookahead. • The results show that bitrates below 32 kbit/s are not useable for voice applications.The voice quality is much worse than with SILK or bitrates shown in steady state
Chain Replication for Supporting High Throughput and Availability
(2004)

Chain replication is a new approach to coordinating clusters of fail-stop storage servers. The approach is intended for supporting large-scale storage services that exhibit high throughput and availability without sacrificing strong consistency guarantees. Besides outlining the chain replication protocols themselves, simulation experiments explore the performance characteristics of a prototype implementation. Throughput, availability, and several objectplacement strategies (including schemes based on distributed hash table routing) are discussed.
Deep Voice 2 : Multi-Speaker Neural Text-to-Speech
(2017)

We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.
A Literature Review on Kidney Disease Prediction using Data Mining Classification Technique
(2014)

-The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The Healthcare industry is generally “information rich”, which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. Kidney disease is a complex task which requires much experience and knowledge. Kidney disease is a silent killer in developed countries and one of the main contributors to disease burden in developing countries. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The Data mining classification techniques, namely Decision trees, ANN, Naive Bayes are analyzed on Kidney disease data set. Keywords--Data Mining, Kidney Disease, Decision tree, Naive Bayes, ANN, K-NN, SVM, Rough Set, Logistic Regression, Genetic Algorithms (GAs) / Evolutionary Programming (EP), Clustering
Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera
(2016)

We propose a method which can perform real-time 3D reconstruction from a single hand-held event camera with no additional sensing, and works in unstructured scenes of which it has no prior knowledge. It is based on three decoupled probabilistic filters, each estimating 6-DoF camera motion, scene logarithmic (log) intensity gradient and scene inverse depth relative to a keyframe, and we build a real-time graph of these to track and model over an extended local workspace. We also upgrade the gradient estimate for each keyframe into an intensity image, allowing us to recover a real-time video-like intensity sequence with spatial and temporal super-resolution from the low bit-rate input event stream. To the best of our knowledge, this is the first algorithm provably able to track a general 6D motion along with reconstruction of arbitrary structure including its intensity and the reconstruction of grayscale video that exclusively relies on event camera data.
Multiple ramp schemes
(1999)

A (t; k; n; S) ramp scheme is a protocol to distribute a secret s chosen inS among a setP of n participants in such a way that: 1) sets of participants of cardinality greater than or equal to k can reconstruct the secrets; 2) sets of participants of cardinality less than or equal tot have no information on s, whereas 3) sets of participants of cardinality greater than t and less thank might have “some” information on s. In this correspondence we analyze multiple ramp schemes, which are protocols to share many secrets among a set P of participants, using different ramp schemes. In particular, we prove a tight lower bound on the size of the shares held by each participant and on the dealer’s randomness in multiple ramp schemes.
Protein function in precision medicine: deep understanding with machine learning.
(2016)

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both.
The impact of design management and process management on quality : an empirical investigation
(2000)

Ž . Design management and process management are two important elements of total quality management TQM implementation. They are drastically different in their targets of improvement, visibility, and techniques. In this paper, we establish a framework for identifying the synergistic linkages of design and process management to the operational quality Ž . Ž . outcomes during the manufacturing process internal quality and upon the field usage of the products external quality . Through a study of quality practices in 418 manufacturing plants from multiple industries, we empirically demonstrate that both design and process management efforts have an equal positive impact on internal quality outcomes such as scrap, rework, defects, performance, and external quality outcomes such as complaints, warranty, litigation, market share. A detailed contingency analysis shows that the proposed model of synergies between design and process management holds true for large and small firms; for firms with different levels of TQM experience; and in different industries with varying levels of competition, logistical complexity of production, or production process characteristics. Finally, the results also suggest that organizational learning enables mature TQM firms to implement both design and process efforts more rigorously and their synergy helps these firms to attain better quality outcomes. These findings indicate that, to attain superior quality outcomes, firms need to balance their design and process management efforts and persevere with long-term Ž . implementation of these efforts. Because the study spans all of the manufacturing sectors SIC 20 through 39 , these conclusions should help firms in any industry revisit their priorities in terms of the relative efforts in design management and process management. q 2000 Elsevier Science B.V. All rights reserved.
A Web Service Discovery Approach Based on Common Topic Groups Extraction
(2017)

Web services have attracted much attention from distributed application designers and developers because of their roles in abstraction and interoperability among heterogeneous software systems, and a growing number of distributed software applications have been published as Web services on the Internet. Faced with the increasing numbers of Web services and service users, researchers in the services computing field have attempted to address a challenging issue, i.e., how to quickly find the suitable ones according to user queries. Many previous studies have been reported towards this direction. In this paper, a novel Web service discovery approach based on topic models is presented. The proposed approach mines common topic groups from the service-topic distribution matrix generated by topic modeling, and the extracted common topic groups can then be leveraged to match user queries to relevant Web services, so as to make a better trade-off between the accuracy of service discovery and the number of candidate Web services. Experiment results conducted on two publicly-available data sets demonstrate that, compared with several widely used approaches, the proposed approach can maintain the performance of service discovery at an elevated level by greatly decreasing the number of candidate Web services, thus leading to faster response time.
Computational Capabilities of Graph Neural Networks
(2009)

In this paper, we will consider the approximation properties of a recently introduced neural network model called graph neural network (GNN), which can be used to process-structured data inputs, e.g., acyclic graphs, cyclic graphs, and directed or undirected graphs. This class of neural networks implements a function tau(G, n) isin R m that maps a graph G and one of its nodes n onto an m-dimensional Euclidean space. We characterize the functions that can be approximated by GNNs, in probability, up to any prescribed degree of precision. This set contains the maps that satisfy a property called preservation of the unfolding equivalence, and includes most of the practically useful functions on graphs; the only known exception is when the input graph contains particular patterns of symmetries when unfolding equivalence may not be preserved. The result can be considered an extension of the universal approximation property established for the classic feedforward neural networks (FNNs). Some experimental examples are used to show the computational capabilities of the proposed model.
Hipikat: a project memory for software development
(2005)

Sociological and technical difficulties, such as a lack of informal encounters, can make it difficult for new members of noncollocated software development teams to learn from their more experienced colleagues. To address this situation, we have developed a tool, named Hipikat that provides developers with efficient and effective access to the group memory for a software development project that is implicitly formed by all of the artifacts produced during the development. This project memory is built automatically with little or no change to existing work practices. After describing the Hipikat tool, we present two studies investigating Hipikat's usefulness in software modification tasks. One study evaluated the usefulness of Hipikat's recommendations on a sample of 20 modification tasks performed on the Eclipse Java IDE during the development of release 2.1 of the Eclipse software. We describe the study, present quantitative measures of Hipikat's performance, and describe in detail three cases that illustrate a range of issues that we have identified in the results. In the other study, we evaluated whether software developers who are new to a project can benefit from the artifacts that Hipikat recommends from the project memory. We describe the study, present qualitative observations, and suggest implications of using project memory as a learning aid for project newcomers.
A Metrics Suite for Object Oriented Design
(1994)

Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field’s understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber, the theoretical base chosen for the metrics was the ontology of Bunge. Six design metrics are developed, and then analytically evaluated against Weyuker’s proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement. “A Metrics Suite For Object Oriented Design” Shyam R. Chidamber Chris F. Kemerer Index Terms CR